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The Key Perfomance Indicators(KPI)/Measurable Objectives setting process triggers the discussion as to whether the way KPI’s are done within a company adds or destroys value. A key plank of a lean-agile approach is systems thinking i.e. focus on optimising the whole end-2-end process, not the individual parts which are KPI’s often do. Deming, who you might say is father of this way of thinking, was specifically against the way to do KPIs. Number 12 in his 14 key principles is:

Remove barriers that rob people in management and in engineering of their right to pride of workmanship. This means, inter alia, abolishment of the annual or merit rating and of management by objective

Enough! It’s easy to criticize KPIs – it’s better to improve. Here’s a summary of the usual suspects and how they can be improved upon.

Variable

Typical measure

Usual outcome

Alternative measures

Time

Delivering on a predicted date

Incentivises hidden time buffers and slower delivery

Maximise speed in getting to the point where value starts to be realised

Scope

Delivering all of the originally predicted scope

Incentivises gold plating and discourages exploitation of learning.

Minimize size of work packages to maximize both learning and early release of value

Cost

Delivering at or below a predicted development cost

Incentivises hidden cost contingencies, pushing costs up.

Maximize value delivered (trade development cost against the opportunity cost of delay)

Quality

Delivering changes with zero downtime and no errors

Fear of change. Overinvestment in testing and documentation.

Shorten feedback cycles at many levels (coding, defects…)

In short, the suggestion is that by over-focussing on the typical measures in the table above, we get a pipeline which is slow, expensive and wasteful. Explore the alternative measures instead!

Try…

Perhaps the table above can be used for inspiration when setting KPIs in your team?

Don’t we just get frustrated when our estimates or guesstimates on timescales or costs turn out to be different from what actually happens? So how do we improve the accuracy of our estimates?

What do we need estimates for?

This is a golden question – ask yourself this whenever you do an estimate. It’s a lean principle to remove waste (i.e. non-value adding activity). If the estimate information you are producing isn’t vital for making a decision, then take a good hard look as to whether you need it. Some of the teams I have worked with have stopped some kinds of estimates as these estimates were not changing the decision.

Why are estimates uncertain?

Estimates are uncertain because we lack information. We’ve never built this particular feature before. We don’t exactly know what we need to do. As we work further with the feature, we gain more information and our uncertainty about how much work is required reduces. This leads to the idea of the cone of uncertainty shown below which suggests that uncertainty decreases (and hence accuracy of estimate increases) throughout the life of a feature.

What helps improve estimating accuracy?

1. Break work down

If you only do one thing, break the work down into smaller pieces (this is a core lean principle!) It’s much easier to estimate smaller activities than larger ones. By simply breaking down large work items into smaller pieces, your estimating accuracy will improve.

2. Increase the rate at which the estimator gains experience

Ability to estimate comes from learning from past experience of estimating. So get more experience! The way you increase your learning rate is to shorten the cycle time i.e. the time it takes to produce something. Doubling the cycle time doubles the rate of learning! Of course, the experience gained needs to be effectively captured (retrospectives, low staff turnover, etc.).

3. Ensure estimators are the people who are doing the work

Seems simple but in a lot of cases, the people doing the estimating are far from the actual work (or, sometimes, estimates are adjusted by people far from the work!)

4. Reduce dependencies

Typically if a task requires 90% of one person and 10% of another, the 10% person will be the bottleneck because his focus is not on the task. This means that it’s difficult for him/her to be available precisely when needed. Eliminating this dependency will reduce variation in timelines. Feature teams help with this.

5. Get multiple inputs

Agile teams often use planning poker to harness the brainpower of the entire team to improve the estimate – it’s unlikely that one person has all the best information. This is a fun and easy thing to try.

6. Agree on which estimate we want

Many timeline estimates are simply the earliest possible time for which the probability of delivery is not zero. Did you want the most likely date (50% chance of being early, 50% chance of being late) or the date for which it is almost certain delivery will have happened by? Make sure everybody agrees which estimate we are talking about.

7. Understand the politics

There are also all sorts of human dynamics that can skew your estimate. For example:

If there are “punishments” for getting it wrong, folk tend to add buffers to their estimates.

If folk believe that the uncertainty associated with the estimate will not be communicated clearly (for example to senior management) then they tend to add buffers. A solution to this that a lot of agile teams use when they are doing an early guesstimate is to use “T-shirt” sizing whereby the estimate is classified as Small, Medium, Large or Extra Large. Communicating guesstimates like this makes it clear that they are not very accurate.

If there is already a target declared (“we want this to be under $100k” or, “we want to complete this by June”) then folk tend to be anchored by that and don’t like to say it’s not possible. We all like to please our stakeholders.

Many fascinating studies show that people have a natural subconscious tendency to be overconfident in their estimating skills. If the estimator(s) have little data on past performance to help correct this, then its likely they will underestimate tasks.

Setting expectations on estimate accuracy

It’s tempting to set a team a goal to improve estimate accuracy. The risk with this (which I have seen in some teams) is that the quickest and simplest way of improving estimating accuracy is either to add undeclared buffers or to invest more time and resource before making the estimate. The latter effectively moves estimate creation to later in the lifecycle which destroys the purpose of the estimate (which is to make decisions early on in the lifecycle).

A better way of improving estimating accuracy is to set the team goals on reducing cycle time and reducing the size of items flowing thro’ the pipeline (as per the first two points above). By focusing on these general lean-agile practices, we also get an improvement in estimating accuracy!

Which is most important? To ensure our teams are fully busy or to maximise the value delivered by the development pipeline? I’m hoping to have you consider that there may be times in when it is in the company’s interest to pay people to “look out of the window” for periods of time.

Throughput decreases as capacity utilisation approaches 100%

Think about a highway – cars flow fine when there is little traffic. When the volume of traffic goes above 80%, we start to get traffic jams. As the loading on the highway approaches 100% then we end up almost no flow at all. The overall value delivered (i.e. people getting to where they want to go) is less. Models have been developed which give the underlying maths of why this is. Highway designers have actually taken these model’s to heart and there are now places were cars are held by traffic lights on the sliproad at peak times in order to maintain a smooth flow on the main highway. The funny thing is this improves everybody’s end-2-end journey time including the cars which were held on the sliproad for a period. Its counter-intuitive, as it probably doesn’t feel like that when you are sitting on the sliproad not going anywhere.

A development pipeline has similar characteristics to a highway. The inherent variation in both demand and supply means that throughput is maximised when the pipeline is run on average below full capacity.

An example

One of the teams I coach is effectively doing a whole release which is dominated by a technical upgrade. To simplify the situation, what that means is that it requires the developers and testers but not the analysts. What should the analysts do whilst the developers/testers are busy with this release?

Options are:

Worst option: Do more analysis. Since the bottleneck is not analysis work, all this will do is pile up a lot of work for the developers/testers to work on. This pile will probably never disappear because as soon as the developers remove something from it, the analysts will continue adding a new item. We haven’t increased the value generated by the pipeline but we’ve increased the cycle time and hence decreased agility and speed of feedback/learning. Think of all the management and communication that with be required to look after this pile of half finished work? The overall value generated is almost certainly negative.

Better option: Analysts look out the window for a while. This has no negative effects on the value delivered. Of course, if this is a permanent thing (which in this case it isn’t) we should look into reducing the number of analysts.

Best option: Analysts learn over time how to help with development and test. They may not be the most efficient at these tasks. T-shaped individuals can move to the bottleneck and increase it’s capacity. This would enable the technical release to be ready earlier and hence increases the value delivered by the pipeline.

It’s counter-intuitive for us to accept that there are situations where the most value people can generate in the short-term is to do nothing. We all like to be busy. It’s not about the productivity of the individual but the productivity of the pipeline as a whole. What do you think?

Retrospectives are a practice used by a lot of software teams – particularly popular with those using SCRUM, since the “sprint retrospective” is part of the SCRUM methodology. They are a effective and simple framework for continuous improvement (Kaizen) for any team – not just those delivering software.

How retrospectives work

The team meets to look at what they have been doing and what they can do differently going forward. Mindset is important – in order for team learning to be effective, there is no room for finger-pointing. The retrospective prime directive captures this:

“Regardless of what we discover, we understand and truly believe that everyone did the best job they could, given what they knew at the time, their skills and abilities, the resources available, and the situation at hand.”

Frequency of retrospectives

Many teams organise a “lessons learnt” session after the project is done (or after a major release). This is too late! It’s too late to change anything, some events have been forgotten and energy levels are low. Retrospectives need to be scheduled at least monthly.

How retrospectives work in our coaching team?

We “eat our own dog food” so we have always had monthly retrospectives in the coaches team. For reasons that are lost in the mists of time (creating a relaxed environment maybe?) we always hold our retrospectives in a bar. The facilitator of the session is not our team leader (me) but is the same as the person who is facilitating our team kanban board (a role which rotates so that all team members get to do it sooner or later).

As a warm up, we create a timeline of the key events which have happened since the last retrospective – handy to bring what’s been going on to the front of our minds:

We then answer the 4 key questions – there are other possible sets of questions out there which we have experimented with (e.g. the retrospective starfish) but these four seem to work best for us:

What did we do well, that if we don’t discuss we might forget?

What did we learn?

What should we do differently next time?

What still puzzles us?

We are very low tech – we have one sheet of A3, divided into four areas, which we all write on at the same time.

When we are all finished writing, we go fairly slowly through everything everybody has written and agree any actions which we transfer to our team kanban board. The whole thing normally takes us about 2 hours – we’ve tried to do it in less but it just hasn’t worked out for us.

So, lets cut to the chase. What have we got of value out this? I’d say two things. Firstly the concrete actions we take as a result of the retrospective have improved the way we work. This has been in many, many small ways. Also, by including the whole team, we have perhaps identified earlier than we otherwise things that are about to become large problems but right now are just small irritations. Secondly, it has helped instil a mindset in which the whole team is responsible for improving the way we work and given us a sense of common ownership for our team process. It is taking us closer to that ever elusive goal: the self-managed team.

Try…

So does your team have a structure for regularly taking time together to look backwards to work out how best to go forwards? Do you like the idea of continuously improving the way the team is working but you don’t know how to approach this? Why not try running retrospectives with your team?

Technical debt is one of the great “inventions” in software engineering of the 1990s. The idea, if you are not familiar with it, is that every time you cut a corner in the interest of delivering value now i.e. delivering on the immediate needs of the project, you record it as a debt. The reason it is a debt, is that if you don’t pay it back then it will impede your ability to deliver value in the future. Examples of technical debt include:

Incurring technical debt can be the right thing to do, providing its incurred in a prudent and deliberate manner.

A fascinating reason for taking debt repayments seriously is the broken window theory. The theory has it’s roots in experiments made in the 80’s. Experiments showed that disorder and crime are linked – if a windows in a building is broken and left unrepaired, all the rest will soon be broken. People seemingly get the message that nobody cares. The insanely complex nature of software systems means that similar behaviour is observed – nothing accelerates the speed of the descent into un-maintainability than allowing technical debt to build up.

Process-wise, what is required is:

A process for recording technical debt and making it visible to stakeholders

A process for deciding when to paying it back

The first bit is easy. The second is tricky. The solution for the second bit that some teams I have worked with hasve settled on allocating 50% of the development effort to paying down technical debt. Why 50%? Why not 40% or 60%. I’m guessing that 50% was the maximum the team felt they could ask for without having an unacceptable impact on the delivery of value now.

A better approach would be to use an economics-based prioritisation algorithm (like cost-of-delay divided by duration). Its not easy, since we would need to estimate the benefits/savings from paying down each element of the debt, but it’s worth it. An effective technical debt process avoids creating solutions that, just a few years after they were first created, we can no longer effectively change because the cost and risk is too high.

Here’s a interesting real-life story of a requirement from one of the delivery teams that I have worked with which emphasises how a focus on cost-of-delay helps.

The story concerns requirement RQ-0672 which took 46 weeks to go from being captured as a idea to being live in production. We have a fair idea of which weeks there were activities happening (green) and which weeks nothing happened (red); shown in the value stream map below (See here to learn more about value stream mapping techniques):

Observation 1: It’s pretty clear there was a lot of “waiting waste”. Most weeks, nothing happened!

We introduced the concept of cost-of-delay to this team – i.e. measuring the opportunity cost to the company for every week this requirement was not implemented. This turned out to be $214,000 per week. Cost-of-delay figures often have high levels of uncertainty (particularly if the benefits of the requirements are around increasing revenue as in this case) but in this case, this number is fairly certain.

This particular delivery stream is capable of delivering a requirement like this within 13 weeks. Had this been delivered within 13 weeks instead of 46 i.e. 33 weeks earlier, this would have improved the company’s revenue by:

$214,000 x 33 = $7,062,000

Wow. Big number, particularly in the content the actual cost of making the change was around $4,200.

Obseveration 2: There can be significant value in reducing cycle time.

So why didn’t we make it happen earlier? The pipeline was setup without a focus on cost-of-delay which means there is no sense of the urgency associated with each requirement – they are all treated the same. Once we know the urgency, then we know what trade-offs to make.

In this case, you could say that it makes sense for the company overall to spend up to $214,000 for every week we could bring forward the go-live date for this requirement. You can also see from the value stream mapping that there was a proof-of-concept done for this requirement. This proof-of-concept took a week to execute – plus some mobilisation time – probably several weeks – hard to know exactly. Lets say 2 weeks mobilisation for the sake of argument. The cost of delaying the go-live by 3 weeks to do the proof-of-concept was:

3 x $214,000 = $642,000

The purpose of this proof-of-concept was to be able to accurately estimate the cost of the change. Remember, this change actually came in at a cost of $4,200. Given the enormous benefits, it would still have made sense for the company to implement this change if the cost had come in at $4,200,000. Perhaps we should have skipped the proof-of-concept stage for this requirement!

Observation 3: Not all estimating activity is truly value-adding

I have selected an extreme example of course. Even so, its real. It happened. It serves to emphasis that some requirements have high cost-of-delay which needs to be managed. A good innovation process design should expedite these types of requirements by:

Prioritising quickly based on cost-of-delay (ideally within a week)

A frequent “pull” of the top requirement from the top of the dynamic prioritised list of requirement

Fast cycle time from the time of “pull” to go-live through a pipeline which is not clogged up with too many other requirements

An interesting area of discussion is to whether a lean-agile approach contributes anything to effective risk management. Do lean-agile practices effectively mitigate risk? Some say yes and some say no.

No!

There are, I believe, currently no lean-agile practices which are designed to identify the most important risks and then focus on mitigating and tracking them. Other (traditional) project management approaches have tools and practices for this – for example by the creation and ongoing review of a risk log. Yet this is missing in the lean-agile toolkit. Lean-agile practices need supplementing by a traditional risk management practice.

Yes!

The traditional risk log approach is of limited effectiveness. If you ask a team what the top three risks the team faces are (go on, try it), it’s rare that the team comes with a consistent answer. Even when a risk log helps identify risks, analysing the true root cause of a risk in any particular case can be difficult.

By implementing lean-agile practices, teams get access to ways of mitigating the highest risks that an IT development project faces as evaluated by broad industry experience.

Take the Agile Manifesto. It was created by a group of experienced people. Apparently, it was tough to find things they agreed on (even though they were experienced, the number of projects they have worked on is still small so the number of data points they have is also small). They did manage to agree on the agile principles behind the manifesto. Each principle addresses one or more key underlying risk facing an IT development project. I’ve had a go at identifying the implied risk below. Would you think of these when you start a IT development project? Would you agree that these are the key risks?

Principle

Implied risk

Our highest priority is to satisfy the customer
through early and continuous delivery
of valuable software.

Solution deliver generates no or little value. Unused features.

Deliver working software frequently, from a
couple of weeks to a couple of months, with a
preference to the shorter timescale.

Solution could not be built as intended. Solution delivers no value. Things external to the project change.

Business people and developers must work
together daily throughout the project.

Business people and developers do not communicate quickly & effectively.

Build projects around motivated individuals.
Give them the environment and support they need,
and trust them to get the job done

Environments and support not available.
Teams not empowered.

The most efficient and effective method of
conveying information to and within a development
team is face-to-face conversation.

Geographical distribution of teams and over-reliance on electronic communication leads to ineffective communication.

Working software is the primary measure of progress.

Focus on internal goals instead of meaningful ones (delivery of working software)

Agile processes promote sustainable development.
The sponsors, developers, and users should be able
to maintain a constant pace indefinitely.

Unsustainable development (pace, architecture, …)

Continuous attention to technical excellence
and good design enhances agility.

Lack of focus on technical excellence.

Simplicity–the art of maximizing the amount
of work not done–is essential.

Complex code. Doing work that doesn’t need to be done.

The best architectures, requirements, and designs
emerge from self-organizing teams.

Ineffective teams due to poor team structure/task management and the team not being empowered to change this.

I have been looking recently at managing requirements for new projects. I thought it would be interesting to share our top lean-agile tips/encouragements for for managers of requirements (i.e. the product owner role in agile terminology):

Tip #1: Do only enough requirements work up front to enable you to build the first (small) feature. Then learn and adjust!

How much do we need to work out up front and how much can evolve as we build the solution? Defining all the requirements work up front (Big Requirements Up Front) seems attractive. It means that there can be a contractual style relationship between business folk and solution builders. Commitments can be made and, if an external vendor is involved, fixed price deals can be made. If dates, budgets etc. are not met then punishment can be handed out etc.

Yet this is not the way the IT industry is going. The biggest risk in IT development is that the solution delivers little or no value (unused features). The whole agile movement has this in focus. The IT industry has learnt that Big Requirements Up Front actually increase this risk because the delay the point when we are going to get feedback on our requirements. There are always large uncertainties in what the customer needs and how to build it. Its never been done before! A lean-agile approach emphasises learning – build a bit and get a response so we don’t have to get it right first time.

Wait, I hear you say. Doesn’t this mean that you might not take all business requirements into account from the start and have to refactor/reimplement certain features later? Yep, it probably does mean this. IT industry experience suggests that these refactoring costs are typically much smaller than the costs required to get it right first time. (See here for more details).

Tip #2: See requirements as a placeholder for a conversation, not the conversation itself.

Consider requirements as a placeholder for a conversation between the people who have a business need and the developer writing the code. They are not the conversation itself.

To align everybody around the direction we want to head in, a short up-to-date document describing the project vision (better a short document which everybody has read than a longer one that hasn’t been read at all). Beyond this, user stories are usually the best format to capture requirements since they capture the intention of the requirement (“in order to”) and who wants this requirement (“as a”). Otherwise, these things are often lost. e.g.

AS A: Flickr member

I WANT TO: be able to assign different privacy levels to my photos

IN ORDER TO: control who I share which photos with

Tip #3: Break requirements down

User stories can be arranged into hierarchies with top level stories which are broken down into smaller stories. Keep breaking the work down until you get to a feature that can’t be broken any further and still retain some business value (a minimal marketable feature in the jargon).

Tip #4: Define acceptance criteria up front

How will we know when a requirement has been delivered? Ensure user stories have acceptance criteria so we know when we are done.

AS A: Flickr member

I WANT TO: be able to assign different privacy levels to my photos

IN ORDER TO: control who I share which photos with

ACCEPTANCE CRITERIA

-

A user cannot submit a form without completing all the mandatory fields
– Information from the form is stored in the registrations database
– Protection against spam is working
– Payment can be made via credit card
– An acknowledgment email is sent to the user after submitting the form.

Tip #5: Collaborate like crazy, preferably face-2-face

Two of the the principles behind the agile manifesto are: “the most efficient and effective method of conveying information to and within a development team is face-to-face conversation.” and “Business people and developers must work together daily throughout the project” Geographic constraints may hinder this but try and get as close to this ideal as you can. Be available throughout development/testing/ etc. for questions (e.g. attend the daily stand-up). If you don’t do this, the fast feedback model won’t work.

Tip #6: Use CD3 for prioritising and be prepared to stop early

Use the economic model cost-of-delay divided by duration as the basis for prioritising requirements. This also means that we have the option to stop early when we have delivered the most valuable requirements.

Tip #7: Smooth the end2end requirements flow

Requirements need to flow into a development team. There is no point developing requirements too early (requirements go stale very quickly). Equally, there is no point having a development team standing idle. Ensure the people who are actually going to build the solution (i.e. coders, not managers) participate the the refining of the user stories. It these people are not available now, wait until they are. It’s counterproductive to create a large queue of requirements that are waiting to be implemented.

Tip #8: Explain to business stakeholders how they need to behave

It’s helpful to explain to business stakeholders what kind of behaviour from them would support a successful outcome:

Charter the team to deliver the best possible value in a fixed time scale. Be cautious about asking for a fixed scope by a fixed date. The team will probably have to do Big-Requirements-Up-Front (BRUF) in order to have enough information to commit which significantly raises the risk of failure. It may also mean that invisible factors, like quality, are compromised.

Provide $ cost-of-delay/week and trust the team to make the right trade offs. Saying that “this is top priority” or “the board wants this” etc., gives the team no guidance about how to trade off against other high priority items.

Trust the team.

Make sure business people are available to quickly give feedback.

Insist on getting to see something of business value working in production quickly (30-90 days from project start) .

Insist on a weekly demonstration of progress.

Insist on regularly attending the team stand-up.

Insist that the team commit to dates on near-term work items (typically for the next 2-4 weeks).

Insist on an up-to-date roadmap covering a longer period. Don’t expect a lot of detail on this.

I was reflecting on why we don’t all simply do things in a lean-agile way by default. Why are short iterations, fast feedback and a collaborative working style not the natural way of doing things? What is so attractive about organising the development process around a single pass, stage gated approach (typically referred to as “waterfall”) that we often need a whole change programme to address it?

H. L. Mencken quipped, “For every complex problem, there is a solution that is simple, neat, and wrong.” One root cause of waterfall’s historical popularity could be that software projects have been inappropriately associated with a predictable manufacturing paradigm (such as manufacturing TVs or cars). In this paradigm, things can be predictably specified and planned, in contrast to the the chaos and uncertainty associated with a new product development paradigm. Since predictable manufacturing is the wrong paradigm for software, practices and values rooted in it are not helpful. Two assumptions in particular is are a source of amusement or dismay to developers of software solutions:

There is a single set of requirements and delivery options that describe what the business wants to do (requirements) and how it will be delivered (analysis and design).

The specifications do not change, or only change minimally (and perhaps predictably) during the construction and test phases of the project, subject to a strong change management process.

The deep appreciation—that building software is complex, new product development with high change rates, and not predictable manufacturing—is at the heart of the motivation for lean-agile methods.

Another possible factor is that single-pass waterfall gives the illusion of an orderly, predictable, accountable, and measurable process, with simple document-driven milestones (such as “requirements complete”). There is a special irony in choosing a simple-to-track process that yields higher levels of risk since the high risk activities – testing, integration and most of all, finding out whether the solution is valuable, are pushed to the end.

Software development is a young field, so it is no surprise the simple formula of “requirements, design, implementation” have dominated during the first attempts to create a skilful development process. The single-pass waterfall has the simplicity of explanation and recall (“do the requirements, then design, and then implement”);

So is the IT industry abandoning waterfall as it matures? Last month Gartner (in a research note entitled “The End of the Waterfall as We Know It” – requires a Gartner subscription) confirmed that waterfall is still the dominant approach across the industry (52% of development projects). They go on to say…

“Quite simply, waterfall methods, when used in the traditional, project-based manner, are inconsistent and risky. Since there are other choices available that have the potential to be more consistent and less risky, it’s time to begin the transition to these methods.”

Seems to me that we are reaching the tipping point as the IT industry moves to adopting lean and agile approaches!

Our highest priority is to satisfy the customer through early and continuous delivery of valuable software.

Working software is the primary measure of progress.

So it seems like these metrics will promote these principles. There is no standard on how to measure these things practically, or who the customer is, or when to start the clock on cycle time, or how to quantify business value, or how to avoid nasty behavioural side-effects. One particularly difficult area is to decide when business value is delivered. Is this when a feature is actually been used or is when the feature is ready to launch in production but is held back because of a business decision (know as earned business value in IT industry jargon)?

What else? All experts say it’s better to have a small number of metrics. Even so, there are things other lean-agile practitioners like to add on top of this. Perhaps these are of primary interest to the delivery/portfolio manager/delivery team but not beyond that:

Velocity – This is equivalent to throughput – i.e. how much functionality is delivered per month. It’s hard to measure if one doesn’t have a stable way of estimating the size/complexity of requirements and it’s impossible to compare across delivery streams.

Defects/Errors – Both open and fixed.

Unit test coverage – For all common programming languages there are tools which can calculate what percentage of the lines of code are executed by the unit tests. Perhaps this does need more focus as coverage in many of the teams I have worked with is 0% (depending on the language/feature, min 80% coverage is normal) and there are very few teams who actually know what their coverage is.

Technical debt – How much has been added and how much has been removed per month. Most of the teams I have worked with don’t track technical debt so that makes this an interesting one to work on.

Work in progress – This can be visualised on the team kanban board. Work-in-progress and queuing times are leading indicator for cycle time (whereas cycle time is always a lagging indicator) which allows action earlier.

Lean-agile practice adoption maturity

Perhaps this gives some inspiration to adjusting key performance indicators next time round?